Embodiments of the invention are directed, in general, to navigation systems and, more specifically, to global navigation satellite system (GNSS) and inertial measurement unit (IMU) integration.
Any satellite-based navigation system suffers significant performance degradation when satellite signal is blocked, attenuated and/or reflected (multipath), for example, indoor and in urban canyons. As MEMS (micro-electro-mechanical systems) technologies advance, it becomes more interesting to integrate sensor-based inertial navigation system (INS) solutions into GNSS receivers, in pedestrian applications as well as in vehicle applications.
As GNSS receivers become more common, users continue to expect improved performance in increasingly difficult scenarios. GNSS receivers may process signals from one or more satellites from one or more different satellite systems. Currently existing satellite systems include global positioning system (GPS), and the Russian global navigation satellite system (Russian: , abbreviation of    ; tr.: GLObal'naya NAvigatsionnaya Sputnikovaya Sistema; “GLObal NAvigation Satellite System” (GLONASS). Systems expected to become operational in the near future include Galileo, quasi-zenith satellite system (QZSS), and the Chinese system Beidou. For many years, inertial navigation systems have been used in high-cost applications such as airplanes to aid GNSS receivers in difficult environments. One example that uses inertial sensors to allow improved carrier-phase tracking may be found in A. Soloviev, S. Gunawardena, and F. van Graas, “Deeply integrated GPS/Low-cost IMU for low CNR signal processing: concept description and in-flight demonstration,” Journal of the Institute of Navigation, vol. 55, No. 1, Spring 2008; incorporated herein by reference. The recent trend is to try to integrate a GNSS receiver with low-cost inertial sensors to improve performance when many or all satellite signals are severely attenuated or otherwise unavailable. The high-cost and low-cost applications for these inertial sensors are very different because of the quality and kinds of sensors that are available. The problem is to find ways that inexpensive or low-cost sensors can provide useful information to the GNSS receiver.
The inertial measurement unit (IMU) may include any of the following: accelerometers, magnetometers, and/or gyroscopes. And the IMU provides independent navigation information regardless of the GNSS signal condition. In many commercial applications, low-accuracy inertial sensors are used because of cost constraint. This invention provides methods for estimating and compensating the navigation error due to using low-quality IMU, while integrating the IMU-based measurements with the GNSS-based measurements.
Sources of Dead Reckoning Errors
For pedestrian navigation, pedestrian dead reckoning (PDR) technique may be implemented because it suffices to use relatively low-accuracy sensors. The PDR is usually based on step detection, step length estimation, and heading determination. PDR encounters the following types of dead reckoning (“DR”) errors.                Speed bias/error: Any inaccuracy in step length estimation results in speed error/bias in the DR measurement (DR measurements refer to INS outputs, such as position or velocity values).        Heading bias/error: Heading error due to soft-iron effect (local magnetic disturbance) is generally difficult to estimate and compensate since it is usually location-dependent. However, relatively large heading bias due to different attitude of IMU (from assumed one) can be estimated and compensated. For example, mounting IMU on right-side of waist will have 90 degrees of heading bias compared with the case of mounting the IMU on back waist.        
Similarly, in vehicular applications, speed and heading biases are commonly observed in the INS measurement. And they are main sources of the error in the final user position and velocity estimate.
Therefore, there is a need for a tightly-coupled blending filters with calibration features built-in to track the speed and heading biases.